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iTASK - Intelligent Traffic Analysis Software Kit

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iTASK - Intelligent Traffic Analysis Software Kit

CVPR AI City Challenge 2020 (HCMUS Team)

Introduction

Our Intelligent Traffic Analysis Software Kit (iTASK) aims to tackle three challenging problems: vehicle flow counting, vehicle re-identification, and abnormal event detection. Experiments on the datasets of traffic flow analysis from AI City Challenge 2020 show our competitive results.

Vehicle Flow Counting

We propose to real-time track vehicles moving along the desired direction in corresponding motion-of-interests (MOIs). Our proposed method achieved S1 score of 0.8297 for vehicle flow counting in Track 1.

Vehicle Re-identification

We consider each vehicle as a document with multiple semantic words (i.e., vehicle attributes) and transform the given problem to classical document retrieval. Our proposed method achieved mAP score of 0.3882 for vehicle re-identification in Track 2.

Abnormal Event Detection

We propose to forward and backward refine anomaly detection using GAN-based future prediction and backward tracking completely stalled vehicle or sudden-change direction, respectively. Our proposed method achieved S4 score of 0.9059 for anomaly detection in Track 4.

Citations

Please consider citing this project in your publications if it helps your research:

@Inproceedings{tmtriet-AICity2020,
  Title          = {iTASK - Intelligent Traffic Analysis Software Kit},
  Author         = {Minh-Triet Tran and Tam V. Nguyen and Trung-Hieu Hoang and Trung-Nghia Le and Khac-Tuan Nguyen and Dat-Thanh Dinh and Thanh-An Nguyen and Hai-Dang Nguyen and Trong-Tung Nguyen and Xuan-Nhat Hoang and Viet-Khoa Vo-Ho and Trong-Le Do and Lam Nguyen and Minh-Quan Le and Hoang-Phuc Nguyen-Dinh and Trong-Thang Pham and Xuan-Vy Nguyen and E-Ro Nguyen and Quoc-Cuong Tran and Hung Tran and Hieu Dao and Mai-Khiem Tran and Quang-Thuc Nguyen and The-Anh Vu-Le and Tien-Phat Nguyen and Gia-Han Diep and Minh N. Do},
  BookTitle      = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
  Year           = {2020},
}

The code is used for academic purpose only.

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  • Jupyter Notebook 68.4%
  • Python 25.1%
  • C++ 3.2%
  • Cuda 1.3%
  • C 1.1%
  • CMake 0.6%
  • Other 0.3%